40 citations · 85 across the 20 of their papers we have counts for
6 papers · 1 filter
Unbiased and Second-Order-Free Training for High-Dimensional PDEs
Jaemin Seo, Surin Lee, Jae Yong Lee
Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics-informed neural networks (PINNs) for solving h…
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation
Jae Yong Lee, Gwang Jae Jung, Byung Chan Lim +1
The Boltzmann equation, a fundamental model in kinetic theory, describes the evolution of particle distribution functions through a nonlinear, high-dimensional collision operator.…
Conformal mapping based Physics-informed neural networks for designing neutral inclusions
Daehee Cho, Hyeonmin Yun, Jaeyong Lee +1
We address the neutral inclusion problem with imperfect boundary conditions, focusing on designing interface functions for inclusions of arbitrary shapes. Traditional Physics-Infor…
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
Sung Woong Cho, Jae Yong Lee, Hyung Ju Hwang
Scientific computing using deep learning has seen significant advancements in recent years. There has been growing interest in models that learn the operator from the parameters of…
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
Jae Yong Lee, Sung Woong Cho, Hyung Ju Hwang
Fast and accurate predictions for complex physical dynamics are a significant challenge across various applications. Real-time prediction on resource-constrained hardware is even m…
Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
Jin Young Shin, Jae Yong Lee, Hyung Ju Hwang
Learning the mapping between two function spaces has garnered considerable research attention. However, learning the solution operator of partial differential equations (PDEs) rema…